Early 2025. A company in the telecommunications infrastructure sector organizes AI training for its teams. Full room, strong interest, people asking questions, the atmosphere promising. At the end of the day, several people say: "We need to implement this." The energy is there.
I encourage continuation — systematic, long-term collaboration. Not a one-time training, but regular work with teams over several months. The proposal doesn't generate enthusiasm. "This is enough for now, let's see how it goes."
Six months passed. They came back. Because AI usage in the company was very low. Operational processes hadn't changed at all. Generative AI wasn't being used in daily work. People who had been full of energy at the training went back to old habits within two weeks.
A Pattern That Repeats Across Every Industry
This is not one company's story. It's a pattern I see regularly — in telecom, in energy, in finance, in manufacturing. The script is always the same:
Training — high attendance, strong interest, people leave motivated.
Euphoria — for a week or two, people experiment. They test ChatGPT, play with prompts, show each other results.
Silence — daily reality wins. Deadlines, meetings, ongoing tasks. AI fades into the background because nobody built it into daily work.
Usage drops — after a month or two, AI is used by a handful of enthusiasts. The rest of the organization works exactly as it did before the training.
Return with the same problem — six months, a year later. "AI doesn't work for us." It doesn't work because nobody worked to make it work.
Why Training Isn't Enough
Training provides knowledge. It doesn't provide change. Between "I know what AI can do" and "I use AI in my daily work" lies a chasm that a two-day workshop won't bridge.
People learn through practice, not presentation. Training shows possibilities. Practice shows value. Until someone sees that AI saves them an hour a week on their specific task, they won't change habits. And for them to see that, they need individual work, not a mass lecture.
Habits are stronger than motivation. The post-training euphoria lasts a week, maybe two. Habits last for years. To change a habit, you need to change the environment in which that habit operates. The process, the tools, the expectations of managers. Training alone doesn't change the environment.
Lack of follow-up signals: "this isn't important." When nobody asks after the training whether people are using AI, nobody offers support, nobody measures adoption — the organization sends a clear message. AI was an event, not a priority. People read that signal perfectly.
The Cost of Deferred Change
Companies that let things slide after training and come back six months later pay double. Not just for a second attempt at the topic — but for something harder to measure: people's disillusionment.
The first time, people were open. They saw potential. They were ready to try. The second time, they hear: "AI again." Another training. Another promise that this time will be different. Skepticism is higher, motivation lower, and the barrier to entry greater. Because people already tried once and nothing came of it — through no fault of their own.
This is the hidden cost of the "train and see" approach. Not financial risk, but organizational risk: people who stop believing the company is serious about change.
What Works Instead of One-Time Training
AI adoption is not a project with a start date and an end date. It's a process of changing how people work that requires time, consistency, and presence.
Small team, real tasks. Instead of training for two hundred people — a workshop for fifteen. Not "what can AI do," but "how AI helps you with this report you produce every Friday." People work on their own data, in their own processes, on their own problems. After such a workshop, twelve out of fifteen change how they work — because they see value, not a demo.
Three to six months of systematic work. Regular meetings, check-ins, problem-solving. Weekly or biweekly. Not because people can't learn — because changing habits requires repetition, support, and course correction.
Changing processes, not just skills. If the process doesn't account for AI, people won't use AI. It's not a motivation issue — it's a structural issue. Add a step in the process, change the template, define expectations. Make AI part of the norm, not an option.
Measuring adoption, not training satisfaction. A post-training survey measures whether people liked it. That has zero correlation with whether they'll change how they work. Measure: how many people use AI after one month, after three months. Which processes have changed. Where people are saving time.
The Difference Between an Event and Change
Training companies sell events. A two-day workshop, a certificate, a group photo. That has its value — people learn that AI exists and what it can do. But that's a starting point, not a destination.
Changing how people work requires someone who stays. Who asks after the training: "What now?" Who checks after a month whether people are using what they learned. Who after three months helps modify the process that's blocking adoption.
I work with teams in a format of workshops embedded in real tasks, because I know that a one-time training doesn't change an organization. Systematic work with the people who work in that organization every day does.
If after an AI training you see that little has changed — it doesn't mean AI doesn't work in your company. It means the real work hasn't started yet. Let's talk about how to begin.